🧰 Tool4Boxology

Tool4Boxology is a modular visual toolkit for designing and validating hybrid AI systems using the Boxology methodology. It provides a shared conceptual and visual language to describe systems that combine symbolic reasoning and machine learning.

This project is inspired by the work of Frank van Harmelen et al. in their paper:

"Modular Design Patterns for Hybrid Learning and Reasoning Systems: A Taxonomy, Patterns and Use Cases" (Web Semantics, 2023)

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🎯 Motivation

Hybrid AI systems lack a common language for design, validation, and communication. Tool4Boxology addresses this by:

• Providing a visual grammar and formal syntax for system components
• Enabling modular, explainable design with reusable patterns
• Offering real-time validation tools to reduce design errors early
• Enhancing documentation, automation, and traceability

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🧩 Key Features

• ✅ Draw.io Plugin with validation logic
• 🧠 Formal grammar for elementary Boxology patterns
• 🧰 Custom vocabulary libraries
• 🖥️ GoJS-based interface for direct interaction
• 🐳 Dockerized setup for reproducible deployment

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📦 Repository Structure

Folder	Description
Boxology-Docker	Docker container setup based on fjudith/drawio, including preloaded plugins and custom libraries.
Boxology-interface	Custom visual interface for Boxology models using GoJS.
Boxology-plugin	Draw.io plugin and vocabulary library for manual integration.
ElementaryPattern	Elementary patterns written in DOT language for modular visualization.
Report	Development report tracing project progress from scratch to implementation.

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🚀 Quick Start

1. Use the Plugin

• Open Draw.io (web or desktop)
• Import the plugin from the Boxology-plugin folder
• Load the vocabulary .drawio file to access custom shapes

2. Run with Docker

git clone https://github.com/SDM-TIB/Tool4Boxology.git
cd Tool4Boxology/Boxology-Docker
docker-compose up

Access Draw.io at http://localhost:8080 with Boxology support.

3. Use the Interface

Open the GoJS-based visual editor from the Boxology-interface folder. Instructions are included in its README.

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📘 Example

A sample hybrid AI pipeline using Boxology grammar. This diagram is a use case from Boxology paper (Robot in Action).

✅ Check for validation!

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🔮 Future Work

• RDF export for integration into knowledge graphs
• SHACL-based validation for semantic consistency
• Enhanced support for reasoning templates and ontology-based rules

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📄 Reference

• Harmelen, F. van, Liao, B., Ifrim, G., & Groth, P. (2023). Modular Design Patterns for Hybrid Learning and Reasoning Systems. Journal of Web Semantics, 79, 100769. DOI
• Keet, C. M., & Rodríguez-Muro, M. (2023). Combining Machine Learning and Semantic Web: A Systematic Mapping Study. ACM Computing Surveys, 55(7), Article 140.

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👤 Author & Contact

📧 Mahsa Forghani Tehrani
🎓 Master Student, Leibniz University Hannover
📮 mahsa.forghani.tehrani@stud.uni-hannover.de

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📜 License

• 🧠 Code is licensed under the MIT License.
• 📄 Documentation, diagrams, and examples are licensed under the Creative Commons Attribution 4.0 International License.

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🤝 Contributing

Pull requests and feedback are welcome! Please open issues for bugs, ideas, or questions.